1 / 52

Intro 1: Last week's take home lessons

Intro 1: Last week's take home lessons. Life & computers : Self-assembly Math: be wary of approximations Catalysis & Replication Differential equations: dy/dt=ky(1-y) Mutation & the single molecule: Noise is overcome Directed graphs & pedigrees

alfonso-collins
Download Presentation

Intro 1: Last week's take home lessons

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Intro 1: Last week's take home lessons Life & computers : Self-assembly Math: be wary of approximations Catalysis & Replication Differential equations: dy/dt=ky(1-y) Mutation & the single molecule: Noise is overcome Directed graphs & pedigrees Bell curve statistics: Binomial, Poisson, Normal Selection & optimality

  2. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  3. For most NA & protein backbones: C,H,N,O,P,S 6+13 Useful for many species: Na, K, Fe, Cl, Ca, Mg, Mo, Mn, Se, Cu, Ni, Co, Si Elements

  4. From atoms to (bio)molecules H2OH2 , O2 H+ , OH- CH4 C60 CO3- NH3 N2 NO3- H2SSnSO4-- Mg++ PH3 K+PO4-- Na+ Gas Elemental Salt

  5. Purify Elements, molecules, assemblies, organelles, cells, organisms Clonal growth chromatography

  6. Purified history Pre 1970s: Column/gel purification revolution Mid-1970s: Recombinant DNA brings clonal (single-step) purity. 1984-2002: Sequencing genomes & automation aids return to whole systems.

  7. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  8. "A New Approach To Decoding Life: Systems Biology" Ideker et al 2001 • Define all components of the system. • 2. Systematically perturb and monitor components of the system (genetically or environmentally). • 3. Refine the model such that its predictions most closely agree with observations. • 4. New perturbation experiments to distinguish among model hypotheses.

  9. Systems biology critique An old approach. New spins: 1. “all components” 2. “Systematically perturb” Unstated opportunities? 3. Refine the model without overfitting. Methods to recapture unautomated data. Explicit(automatic?) logical connections. 4. Optimization of new perturbation experiments & technologies. Automation, ultimate applications, & synthetics as standards for: search, merge, check

  10. Transistors > inverters > registers > binary adders >compilers>application programs Spice simulation of a CMOS inverter (figures)

  11. Why? #0. Why sequence the genome(s)? To allow #1,2,3 below. #1. Why map variation? #2. Why obtain a complete set of human RNAs, proteins & regulatory elements? #3. Why understand comparative genomics and how genomes evolved? To allow #4 below. #4. Why quantitative biosystem models of molecular interactions with multiple levels (atoms to cells to organisms & populations)? To share information. Construction is a test of understanding & to make useful products.

  12. Grand (& useful) Challenges • A) From atoms to evolving minigenome-cells. • Improve in vitro macromolecular synthesis. • Conceptually link atomic (mutational) changes to population evolution • (via molecular & systems modeling). • Novel polymers for smart-materials, mirror-enzymes & drug selection. • B) From cells to tissues. • Model combinations of external signals & genome-programming on expression. • Manipulate stem-cell fate & stability. • Engineer reduction of mutation & cancerous proliferation. • Programmed cells to replace or augment (low toxicity) drugs. • C) From tissues to systems • Programming of cell and tissue morphology. • Quantitate robustness & evolvability. • Engineer sensor-effector feedback networks where macro-morphologies • determine the functions; past (Darwinian) or future (prosthetic).

  13. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  14. Number of component types (guesses) Mycoplasma Worm HumanBases .58M >97M 3000M DNAs 1 7 25 Genes .48k >19k 34k-150k RNAs .4k >30k .2-3M Proteins .6k >50k .3-10M Cells 1 959 1014

  15. From monomers to polymers Complementary surfaces Watson-Crick base pair (Nature April 25, 1953)

  16. Nucleotides dATP rATP

  17. The simplest amino acid component of proteins Glycine Gly G config(glycine,[ substituent(aminoacid_L_backbone), substituent(hyd), linkage(from(aminoacid_L_backbone,car(1)), to(hyd,hyd(1)), nil,single)]). Smiles String: [CH2]([NH3+])[C](=[O])[O-] Klotho

  18. 20 Amino acids of 280 T www.people.virginia.edu/~rjh9u/aminacid.html www-nbrf.georgetown.edu/pirwww/search/textresid.html

  19. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  20. Metabolites RNA Self-assembly, Catalysis, Replication, Mutation, Selection Regulatory & Metabolic Networks Continuity of Life & Central Dogma Interactions DNA Protein Growth rate Expression Polymers: Initiate, Elongate, Terminate, Fold, Modify, Localize, Degrade

  21. "The" Genetic Code F M 3’ uac 5'... aug 3’aag uuu ... Adjacent mRNA codons

  22. Translationt-,m-,r-RNA Ban N, et al. 1999 Nature. 400:841-7. Large macromolecular complexes: Ribosome: 3 RNAs (over 3 kbp plus over 50 different proteins) Science (2000) 289: 878, 905, 920, 3D coordinates. The ribosome is a ribozyme.

  23. Perl Dogma (EditPlus)

  24. Continuity & Diversity of life Genomes 0.5 to 7 Mbp 10 Mbp to 1000 Gbp figure

  25. How many living species? 5000 bacterial species per gram of soil (<70% DNA bp identity) Millions of non-microbial species (& dropping) Whole genomes: 45 done since 1995, 322 in the pipeline! (ref) Sequence bits: 16234 (in 1995) to 79961 species (in 2000) NCBI & Why study more than one species? Comparisons allow discrimination of subtle functional constraints.

  26. Genetic codes (ncbi) 1. "Standard Code" Base1 = TTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCAAAAAAAAAAAAAAAAGGGGGGGGGGGGGGGG Base2 = TTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGGTTTTCCCCAAAAGGGG Base3 = TCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAGTCAG AAs = FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG Starts = ---M---------------M---------------M---------------------------- 2. The Vertebrate Mitochondrial Code AAs = FFLLSSSSYY**CCWWLLLLPPPPHHQQRRRRIIMMTTTTNNKKSS**VVVVAAAADDEEGGGG Starts = --------------------------------MMMM---------------M------------ 3. The Yeast Mitochondrial Code AAs = FFLLSSSSYY**CCWWTTTTPPPPHHQQRRRRIIMMTTTTNNKKSSRRVVVVAAAADDEEGGGG Starts = ----------------------------------MM---------------------------- 11. The Bacterial "Code" AAs = FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG Starts = ---M---------------M------------MMMM---------------M------------ 14. The Flatworm Mitochondrial Code AAs = FFLLSSSSYYY*CCWWLLLLPPPPHHQQRRRRIIIMTTTTNNNKSSSSVVVVAAAADDEEGGGG Starts = -----------------------------------M---------------------------- 22. Scenedesmus obliquus mitochondrial Code AAs = FFLLSS*SYY*LCC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG Starts = -----------------------------------M----------------------------

  27. Translational reprogramming Gesteland, R. F. and J. F. Atkins. 1996. Recoding - Dynamic reprogramming of translation (1996). Ann. Rev.Biochem 65:741-768 Herbst KL, et al. 1994 PNAS 91:12525-9 A mutation in ribosomal protein L9 affects ribosomal hopping during translation of gene 60 from bacteriophage T4. "Ribosomes hop over a 50-nt coding gap during translation..."

  28. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  29. Qualitative biological statements (beliefs) and evidence metabolism cryptic genes information transfer regulation type of regulation genetic unit regulated trigger trigger modulation transport cell processes cell structure location of gene products extrachromosomal DNA sites Riley, GeneProtEC MIPS functions

  30. Gene Ontology (nature of being) The objective of GO is to provide controlled vocabularies for the description of the molecular function, biological process and cellular component of gene products. ... Many aspects of biology are not included (domain structure, 3D structure, evolution, expression, etc.)... small molecules (Klotho or LIGAND )

  31. GO Gene Ontology • Molecular function • What a gene product can do without specifying where or when. (e.g. broad "enzyme" ; narrower "adenylate cyclase“) • Biological process • >1 distinct steps, time, transformation (broad: "signal transduction." narrower: "cAMP biosynthesis.") • Cellular component • part of some larger object, (e.g. ribosome)_

  32. GO Evidence for facts IMP inferred from mutant phenotype IGI genetic interaction IPI physical interaction ISS sequence similarity IDA direct assay IEP expression pattern IEA electronic annotation TAS traceable author statement NAS non-traceable author statement

  33. Direct observation C.elegans cell lineage & neural connections

  34. Capillary electrophoresis (DNA Sequencing) : 0.4Mb/day Chromatography-Mass Spectrometry (eg. peptide LC-ESI-MS) : RP 20Mb/day min Microarray scanners (eg. RNA): m/z 300 Mb/day mpg Other microscopy (e.g. subcell, cell, tissue networks) Sources of Data for BioSystems Modeling:

  35. Signaling PAthway DatabaseSPAD

  36. Dynamic simulation of the human red blood cell metabolic network. Dominant alleles affecting variety of RBC proteins, malaria, drug- hemolysis, etc. Rare individually, common as a group. Jamshidi, et al(2001) Bioinformatics 17: 286-287.

  37. Enzyme Kinetic Expressions Phosphofructokinase

  38. E A EA EB B How do enzymes & substrates formally differ? ATP E2+P ADP E EATP EP Catalysts increase the rate (&specificity) without being consumed.

  39. Metabolites RNA Self-assembly, Catalysis, Replication, Mutation, Selection Regulatory & Metabolic Networks Continuity of Life & Central Dogma Interactions DNA Protein Growth rate Expression Polymers: Initiate, Elongate, Terminate, Fold, Modify, Localize, Degrade

  40. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  41. Capillary electrophoresis (DNA Sequencing) : 0.4Mb/day Chromatography-Mass Spectrometry (eg. peptide LC-ESI-MS) : RP 20Mb/day min Microarray scanners (eg. RNA): m/z 300 Mb/day mpg Other microscopy (e.g. subcell, cell, tissue networks) Sources of Data for BioSystems Modeling:

  42. (the challenge of distant homologs) ? ? Functional Genomics (quantitative ligand interactions) Structural Genomics 100% Sequence Identity: 1. Enolase Enzyme 2. Major Eye Lens Protein 100% Sequence Identity: 1. Thioredoxin Redox 2. DNA Polymerase Processivity

  43. mRNA expression data Coding sequences Non-coding sequence (10% of genome) Affymetrix E. coli oligonucleotide array Spotted microarraympg

  44. What is functional genomics? Function (1): Effects of a mutation on fitness (reproduction) summed over typical environments. Function (2): Kinetic/structural mechanisms. Function (3): Utility for engineering relative to a non-reproductive objective function. Proof : Given the assumptions, the odds are that the hypothesis is wrong less than 5% of the time, keeping in mind (often hidden) multiple hypotheses. Is the hypothesis suggested by one large dataset already answered in another dataset?

  45. Genomics Attitude Whole systems: Less individual gene- or hypothesis-driven experiments; Automation from cells to data to model as a proof of protocol. Quality of data: DNA sequencing raw error: 0.01% to 10%. Consensus of 5 to 10 error: 0.01% (1e-4) Completion: No holes, i.e. regions with data of quality less than a goal (typically set by cost or needs of subsequent projects). Impossible: The cost is higher than reasonable for a given a time-frame and quality assuming no technology breakthroughs. Cost of computing vs. experimental "wet-computers".

  46. Intro 2: Today's story, logic & goals Biological side of Computational Biology • Elements & Purification • Systems Biology & Applications of Models • Life Components & Interconnections • Continuity of Life & Central Dogma • Qualitative Models & Evidence • Functional Genomics & Quantitative models • Mutations & Selection

  47. Mutations and selection Environment Metabolites Interactions RNA DNA Protein Growth rate Expression stem cells cancer cells viruses organisms

  48. Types of Mutants Null: PKU Dosage: Trisomy 21 Conditional (e.g. temperature or chemical) Gain of function: HbS Altered ligand specificity

  49. Multiplex Competitive Growth Experiments t=0

  50. dy/dt = ky Growth & decay y = Aekt ; e = 2.71828... k=rate constant; half-life=loge(2)/k y t

More Related